Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established.

Tensor decomposition-based channel selection for motor imagery-based brain-computer interfaces.

The number of electrode channels in a brain-computer interface (BCI) affects not only its classification performance, but also its convenience in practical applications. Despite many studies on channel selection in motor imagery (MI)-based BCI systems, they consist in matrix analysis of EEG signals, which inevitably loses the interactive information among multiple domains such as space, time and frequency. In this paper, a tensor decomposition-based channel selection (TCS) method is employed for MI BCIs.

[Differences in the Elderly Care Service Demand,Preference,and Tendency Between Urban and Rural Areas in the Pearl River Delta].

Objective To understand the differences in the demand,preference,and tendency for elderly care services between urban and rural areas in the Pearl River Delta (PRD),and to provide reference for the planning and balanced allocation of elderly care resources in urban and rural areas. Methods Using the multi-stage stratified random sampling method,we selected 7 community health service centers in 2 prefecture-level cities in the PRD and conducted a questionnaire survey on the elderly care service demand,preference,and tendency among 1919 regular residents aged 60 years and above who attended the centers. Results A total of 641 urban elderly residents (33.

A high-frequency SSVEP-BCI system based on a 360 Hz refresh rate.

. Steady-state visual evoked potential (SSVEP) based brain-computer interfaces (BCIs) often struggle to balance user experience and system performance. To address this challenge, this study employed stimuli in the 55-62.

A Canonical Correlation Analysis-Based Transfer Learning Framework for Enhancing the Performance of SSVEP-Based BCIs.

A steady-state visual evoked potential (SSVEP)- based brain-computer interface (BCI) can either achieve high classification accuracy in the case of sufficient training data or suppress the training stage at the cost of low accuracy. Although some researches attempted to conquer the dilemma between performance and practicality, a highly effective approach has not yet been established. In this paper, we propose a canonical correlation analysis (CCA)-based transfer learning framework for improving the performance of an SSVEP BCI and reducing its calibration effort.

Intra- and inter-subject common spatial pattern for reducing calibration effort in MI-based BCI.

One major problem limiting the practicality of a brain-computer interface (BCI) is the need for large amount of labeled data to calibrate its classification model. Although the effectiveness of transfer learning (TL) for conquering this problem has been evidenced by many studies, a highly recognized approach has not yet been established. In this paper, we propose a Euclidean alignment (EA)-based Intra- and inter-subject common spatial pattern (EA-IISCSP) algorithm for estimating four spatial filters, which aim at exploiting Intra- and inter-subject similarities and variability to enhance the robustness of feature signals.

Riemannian geometry-based transfer learning for reducing training time in c-VEP BCIs.

One of the main problems that a brain-computer interface (BCI) face is that a training stage is required for acquiring training data to calibrate its classification model just before every use. Transfer learning is a promising method for addressing the problem. In this paper, we propose a Riemannian geometry-based transfer learning algorithm for code modulated visual evoked potential (c-VEP)-based BCIs, which can effectively reduce the calibration time without sacrificing the classification accuracy.

Reducing calibration time in motor imagery-based BCIs by data alignment and empirical mode decomposition.

One of the major reasons that limit the practical applications of a brain-computer interface (BCI) is its long calibration time. In this paper, we propose a novel approach to reducing the calibration time of motor imagery (MI)-based BCIs without sacrificing classification accuracy. The approach aims to augment the training set size of a new subject by generating artificial electroencephalogram (EEG) data from a few training trials initially available.

A Training Data-Driven Canonical Correlation Analysis Algorithm for Designing Spatial Filters to Enhance Performance of SSVEP-Based BCIs.

Canonical correlation analysis (CCA) is an effective spatial filtering algorithm widely used in steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs). In existing CCA methods, training data are used for constructing templates of stimulus targets and the spatial filters are created between the template signals and a single-trial testing signal. The fact that spatial filters rely on testing data, however, results in low classification performance of CCA compared to other state-of-the-art algorithms such as task-related component analysis (TRCA).

Channel selection in motor imaginary-based brain-computer interfaces: a particle swarm optimization algorithm.

The number of electrode channels in a brain-computer interface affects not only its classification performance, but also its convenience in practical applications. However, an effective method for determining the number of channels has not yet been established for motor imagery-based brain-computer interfaces. This paper proposes a novel evolutionary search algorithm, binary quantum-behaved particle swarm optimization, for channel selection, which is implemented in a wrapping manner, coupling common spatial pattern for feature extraction, and support vector machine for classification.

A multi-target brain-computer interface based on code modulated visual evoked potentials.

The number of selectable targets is one of the main factors that affect the performance of a brain-computer interface (BCI). Most existing code modulated visual evoked potential (c-VEP) based BCIs use a single pseudorandom binary sequence and its circularly shifting sequences to modulate different stimulus targets, making the number of selectable targets limited by the length of modulation codes. This paper proposes a novel paradigm for c-VEP BCIs, which divides the stimulus targets into four target groups and each group of targets are modulated by a unique pseudorandom binary code and its circularly shifting codes.

A Novel c-VEP BCI Paradigm for Increasing the Number of Stimulus Targets Based on Grouping Modulation With Different Codes.

In an existing brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP), a method with which to increase the number of targets without increasing code length has not yet been established. In this paper, a novel c-VEP BCI paradigm, namely, grouping modulation with different codes that have good autocorrelation and crosscorrelation properties, is presented to increase the number of targets and information transfer rate (ITR). All stimulus targets are divided into several groups and each group of targets are modulated by a distinct pseudorandom binary code and its circularly shifting codes.

Electrode channel selection based on backtracking search optimization in motor imagery brain-computer interfaces.

Common spatial pattern algorithm is widely used to estimate spatial filters in motor imagery based brain-computer interfaces. However, use of a large number of channels will make common spatial pattern tend to over-fitting and the classification of electroencephalographic signals time-consuming. To overcome these problems, it is necessary to choose an optimal subset of the whole channels to save computational time and improve the classification accuracy.

The backtracking search optimization algorithm for frequency band and time segment selection in motor imagery-based brain-computer interfaces.

Common spatial pattern (CSP) is a powerful algorithm for extracting discriminative brain patterns in motor imagery-based brain-computer interfaces (BCIs). However, its performance depends largely on the subject-specific frequency band and time segment. Accurate selection of most responsive frequency band and time segment remains a crucial problem.

Stimulus Specificity of Brain-Computer Interfaces Based on Code Modulation Visual Evoked Potentials.

A brain-computer interface (BCI) based on code modulated visual evoked potentials (c-VEP) is among the fastest BCIs that have ever been reported, but it has not yet been given a thorough study. In this study, a pseudorandom binary M sequence and its time lag sequences are utilized for modulation of different stimuli and template matching is adopted as the method for target recognition. Five experiments were devised to investigate the effect of stimulus specificity on target recognition and we made an effort to find the optimal stimulus parameters for size, color and proximity of the stimuli, length of modulation sequence and its lag between two adjacent stimuli.

VEP-based brain-computer interfaces modulated by Golay complementary series for improving performance.

Background: The goal of a brain-computer interface (BCI) is to enable communication by pure brain activity without neural and muscle control. However, the practical use of BCIs is limited by low information transfer rate. Recently, code modulation visual evoked potential (c-VEP) based BCIs have exhibited great potential in establishing high-rate communication between the brain and the external world.

Binary particle swarm optimization for frequency band selection in motor imagery based brain-computer interfaces.

A brain-computer interface (BCI) enables people suffering from affective neurological diseases to communicate with the external world. Common spatial pattern (CSP) is an effective algorithm for feature extraction in motor imagery based BCI systems. However, many studies have proved that the performance of CSP depends heavily on the frequency band of EEG signals used for the construction of covariance matrices.

[Simulation research on three dimension electrical impedance tomography and evaluation of image reconstruction quality].

Three dimensional electrical impedance tomography (3D-EIT) became an important branch of EIT recently. It is important to research imaging and image quality evaluation methods for single targets of different positions and multi-targets in 3D field. Using finite element subdivision method, 3D-EIT field was dispersed into cube unit in the present study for models with single target located in the center of field, middle of field, and near to the edge, respectively.

A B3LYP and MP2(full) theoretical investigation into the strength of the C-NO2 bond upon the formation of the molecule-cation interaction between Na+ and the nitro group of nitrotriazole or its methyl derivatives.

The changes of bond dissociation energy (BDE) in the C-NO2 bond and nitro group charge upon the formation of the molecule-cation interaction between Na+ and the nitro group of 14 kinds of nitrotriazoles or methyl derivatives were investigated using the B3LYP and MP2(full) methods with the 6-311++G**, 6-311++G(2df,2p) and aug-cc-pVTZ basis sets. The strength of the C-NO2 bond was enhanced in comparison with that in the isolated nitrotriazole molecule upon the formation of molecule-cation interaction. The increment of the C-NO2 bond dissociation energy (ΔBDE) correlated well with the molecule-cation interaction energy.

Channel selection for optimizing feature extraction in an electrocorticogram-based brain-computer interface.

Feature extractor and classifier are two major components in a brain-computer interface system, in which the feature extractor plays a critical role. To increase the discriminability of features or feature vectors used for classification, it is necessary to select a suitable number of task-related data recording channels. In this article, a machine-learning algorithm is proposed for optimizing feature extraction in an electrocorticogram-based brain-computer interface.

Channel selection by genetic algorithms for classifying single-trial ECoG during motor imagery.

The classification performance of a brain-computer interface (BCI) depends largely on the methods of data recording and feature extraction. The electrocorticogram (ECoG)-based BCIs are a BCI modality that has the potential to achieve high classification accuracy. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery.

Feature extraction and subset selection for classifying single-trial ECoG during motor imagery.

The electrocorticogram (ECoG) recorded from subdural electrodes is a kind of BCI signal source that has the potential to achieve good classification results. The feature extraction and its subset selection are crucial for increasing classification accuracy rate. This paper proposes a new algorithm for classifying single-trial ECoG during motor imagery.

Amplitude and phase coupling measures for feature extraction in an EEG-based brain-computer interface.

Most of the feature extraction methods in existing brain-computer interfaces (BCIs) are based on the dynamic behavior of separate signals, without using the coupling information between different brain regions. In this paper, amplitude and phase coupling measures, quantified by a nonlinear regressive coefficient and phase locking value respectively, were used for feature extraction. The two measures were based on three different coupling methods determined by neurophysiological a priori knowledge, and applied to a small number of electrodes of interest, leading to six feature vectors for classification.